Capturing interactions in pedestrian walking behavior in a discrete choice framework

In this paper we propose a general framework for pedestrian walking behavior, based on discrete choice modeling. Two main behaviors are identified: unconstrained and constrained. The constrained patterns are further classified into attractive interactions and repulsive interactions. The formers are captured by a leader-follower model while the latters through a collision avoidance model. The spatial correlation between the alternatives is taken into account defining a cross nested logit model. Quantitative analysis is performed by maximum likelihood estimation on a real dataset of pedestrian trajectories, manually tracked from video sequences.